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Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance 脑部磁共振成像术后切除腔分割模型及后续治疗辅助工具
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.45609
Sobha Xavier P, Sathish P. K., Raju G
Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures.
脑部磁共振成像(MRI)术后分割本身就具有挑战性,因为脑组织形态各异,难以准确识别切除区域。因此,亟需一种精确的分割模型。由于术后脑部磁共振成像扫描的稀缺性,使用需要大量训练数据的复杂模型并不可行。本文介绍了一种创新方法,用于准确分割和量化核磁共振成像扫描中的术后脑切除腔。所提出的模型被命名为注意力增强 VGG-U-Net 模型,在编码器部分集成了 VGG16 初始权重,并在解码器中集成了自注意力模块,从而提高了术后脑部 MRI 分割的准确性。注意力机制通过集中在特定的感兴趣区域来提高其准确性。VGG16 模型相对较轻,具有预先训练的权重,可从输入中提取极其详细的信息。该模型在公开的术后脑部核磁共振成像数据上进行了训练,Dice 系数值达到了 0.893。然后使用术后脑部核磁共振成像的临床数据集对该模型进行评估。该模型有助于量化切除区域,并能根据术前图像与每个脑区进行比较。该模型的功能有助于放射科医生评估手术成功率并指导后续手术。
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引用次数: 0
Detection of Breast Cancer through the Analysis of Radiographic Images Using Machine Learning: A Systematic Review 利用机器学习分析放射影像检测乳腺癌:系统回顾
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.46791
Kristell Yukie Jimenez Ayala
Breast cancer is an illness that affects many women and can cause even death; this is a case of not being detected on time, which could be due to a human error during the analysis of radiographic images or not going on time in a health center. For this, using machine learning (ML) to analyze radiographic images is proposed as a support tool for radiologists aiming to reduce false diagnostic rates. While researching information, it was detected that this technology has many benefits in the health area; however, it also has limitations or disadvantages. The importance of this paper is to demonstrate that there are not enough clinical tests nor details about the methodologies that were used; there should be more to assert that ML is defined at the moment of making a diagnosis, which generates no conclusive results regarding effectiveness and therefore creates mistrust in doctors, and some people might rather use deep learning (DL) for its application in the detection of breast cancer because DL has more practical tests and fewer limitations than machine learning.
乳腺癌是一种影响许多妇女的疾病,甚至会导致死亡;这是一种未被及时发现的病例,可能是由于在分析放射图像时的人为失误或未按时前往医疗中心。为此,建议使用机器学习(ML)分析放射图像,作为放射科医生的辅助工具,以降低误诊率。在研究信息时,我们发现这项技术在卫生领域有很多好处,但也有局限性或缺点。本文的重要性在于证明了临床测试和所使用方法的细节都不够充分;应更多地断言人工智能是在做出诊断的那一刻定义的,这不会产生有关有效性的结论性结果,因此会造成对医生的不信任,而有些人可能更愿意使用深度学习(DL)来检测乳腺癌,因为与机器学习相比,深度学习有更多的实际测试和更少的局限性。
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引用次数: 0
Proposed Approach for Object Detection and Recognition by Deep Learning Models Using Data Augmentation 利用数据增强的深度学习模型进行物体检测和识别的拟议方法
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.47171
Ismael M. Abdulkareem, Faris K. AL-Shammri, Noor Aldeen A. Khalid, Natiq A. Omran
Object detection and recognition play a crucial role in computer vision applications, ranging from security systems to autonomous vehicles. Deep learning algorithms have shown remarkable performance in these tasks, but they often require large, annotated datasets for training. However, collecting such datasets can be time-consuming and costly. Data augmentation techniques provide a solution to this problem by artificially expanding the training dataset. In this study, we propose a deep learning approach for object detection and recognition that leverages data augmentation techniques. We use deep convolutional neural networks (CNNs) as the underlying architecture, specifically focusing on popular models such as You Only Look Once version 3 (YOLOv3). By augmenting the training data with various transformations, such as rotation, scaling, and flipping, we can effectively increase the diversity and size of the dataset. Our approach not only improves the robustness and generalization of the models but also reduces the risk of overfitting. By training on augmented data, the models can learn to recognize objects from different viewpoints, scales, and orientations, leading to improved accuracy and performance. We conduct extensive experiments on benchmark datasets and evaluate the performance of our approach using standard metrics such as precision, recall, and mean average precision (mAP). The experimental results demonstrate that our data augmentation-based deep learning approach achieves superior object detection and recognition accuracy compared to traditional training methods without data augmentation. We compare the average accuracy of the YOLOv3-SPP model with two other variants of the YOLOv3 algorithm: one with a feature extraction network consisting of 53 convolutional layers and the other with 13 convolutional layers. The average accuracy of the proposed model (YOLOv3-SPP) is reported as accuracy of 97%, F1-score of 96%, precision of 94%, and average Intersection over Union (IoU) of 78.04%.
从安全系统到自动驾驶汽车,物体检测和识别在计算机视觉应用中发挥着至关重要的作用。深度学习算法在这些任务中表现出了不俗的性能,但它们通常需要大量的注释数据集来进行训练。然而,收集此类数据集既费时又费钱。数据增强技术通过人为扩展训练数据集来解决这一问题。在本研究中,我们提出了一种利用数据增强技术进行物体检测和识别的深度学习方法。我们使用深度卷积神经网络(CNN)作为底层架构,特别关注流行的模型,如 You Only Look Once version 3(YOLOv3)。通过对训练数据进行各种变换(如旋转、缩放和翻转),我们可以有效增加数据集的多样性和规模。我们的方法不仅提高了模型的鲁棒性和泛化能力,还降低了过度拟合的风险。通过在增强数据上进行训练,模型可以学会从不同视角、尺度和方向识别物体,从而提高准确性和性能。我们在基准数据集上进行了广泛的实验,并使用精度、召回率和平均精度(mAP)等标准指标评估了我们方法的性能。实验结果表明,与没有数据增强的传统训练方法相比,我们基于数据增强的深度学习方法实现了更高的物体检测和识别准确率。我们将 YOLOv3-SPP 模型的平均精度与 YOLOv3 算法的其他两个变体进行了比较:一个是由 53 个卷积层组成的特征提取网络,另一个是由 13 个卷积层组成的特征提取网络。据报告,拟议模型(YOLOv3-SPP)的平均准确率为 97%,F1 分数为 96%,精确度为 94%,平均联合交叉率 (IoU) 为 78.04%。
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引用次数: 0
Deep Reinforcement Learning Approach for Cyberattack Detection 网络攻击检测的深度强化学习方法
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.48229
Imad Tareq, B. Elbagoury, S. El-Regaily, El-Sayed M. El-Horbaty
Recently, there has been a growing concern regarding the detrimental effects of cyberattacks on both infrastructure and users. Conventional safety measures, such as encryption, firewalls, and intrusion detection, are inadequate to safeguard cyber systems against emerging and evolving threats. To address this issue, researchers have turned to reinforcement learning (RL) as a potential solution for complex decision-making problems in cybersecurity. However, the application of RL faces various obstacles, including a lack of suitable training data, dynamic attack scenarios, and challenges in modeling real-world complexities. This paper suggests applying deep reinforcement learning (DRL), a deep framework, to simulate malicious cyberattacks and enhance cybersecurity. Our framework utilizes an agent-based model that is capable of continuous learning and adaptation within a dynamic network security environment. The agent determines the most optimal course of action based on the network’s state and the corresponding rewards received for its decisions. We present the outcomes of our experimentation with the application of DRL on a specific model, double deep Q-network (DDQN), utilizing policy gradient (PG) on three distinct datasets: NSL-KDD, CIC-IDS-2018, and AWID. Our research demonstrates that DRL can effectively improve cyberattack detection outcomes through our model and specific parameter adjustments.
最近,人们越来越关注网络攻击对基础设施和用户造成的有害影响。传统的安全措施,如加密、防火墙和入侵检测,不足以保护网络系统免受新出现和不断演变的威胁。为解决这一问题,研究人员将强化学习(RL)作为网络安全复杂决策问题的潜在解决方案。然而,强化学习的应用面临着各种障碍,包括缺乏合适的训练数据、动态攻击场景以及对现实世界复杂性建模的挑战。本文建议应用深度强化学习(DRL)这一深度框架来模拟恶意网络攻击并增强网络安全。我们的框架采用基于代理的模型,该模型能够在动态网络安全环境中不断学习和适应。代理根据网络状态和其决策所获得的相应奖励确定最优行动方案。我们介绍了 DRL 在特定模型--双深度 Q 网络(DDQN)--上的应用实验结果,并在三个不同的数据集上利用了策略梯度(PG):NSL-KDD、CIC-IDS-2018 和 AWID。我们的研究表明,通过我们的模型和特定参数调整,DRL 可以有效改善网络攻击检测结果。
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引用次数: 0
Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals 推进非袖带型高血压检测:利用一维卷积神经网络和时域生理信号
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.45547
N. Nuryani, T. P. Utomo, N. Prabowo, Aripriharta, Muhammad Yazid, Mohtar Yunianto
Timely identification of hypertension (HT) is crucial for effectively managing and reducing the potential health consequences, including cardiovascular events such as heart attacks and strokes, as well as the development of kidney disease. Traditional cuff-based devices often discourage regular monitoring because they cause discomfort. Furthermore, the lack of symptoms in HT complicates the early detection of this condition. To address these challenges, our study employs a non-cuff methodology that utilizes unprocessed electrocardiogram (ECG) and photoplethysmogram (PPG) signals. We utilize a customized approach to enhance the features of a one-dimensional convolutional neural network (CNN) specifically tailored to optimize timeseries data. In contrast to previous research, our methodology avoids the need for complex signal extraction or transformation techniques. The main goal is to identify the optimal input signals and fine-tune the critical hyperparameters of CNNs. The clinical data underwent analysis, which revealed that the use of an integrated ECG and PPG approach resulted in the highest level of accuracy for detection. Notably, the F1 score achieved an impressive value of 98.88%. When evaluated separately, ECG outperformed PPG. Our study contributes to the advancement of the field by introducing a new approach that combines comfort and high accuracy in the early detection of HT. This method is practical and ensures a patient-friendly experience.
及时发现高血压(HT)对于有效控制和减少潜在的健康后果至关重要,包括心脏病发作和中风等心血管事件以及肾脏疾病的发展。传统的袖带式设备通常会引起不适,因此不鼓励定期进行监测。此外,高血压没有症状,这也使早期检测变得复杂。为了应对这些挑战,我们的研究采用了一种非袖带方法,利用未经处理的心电图(ECG)和光电搏动图(PPG)信号。我们采用定制方法来增强一维卷积神经网络(CNN)的特征,该网络专门为优化时间序列数据而定制。与以往的研究不同,我们的方法无需复杂的信号提取或转换技术。主要目标是确定最佳输入信号,并微调 CNN 的关键超参数。对临床数据进行分析后发现,使用心电图和 PPG 集成方法的检测准确率最高。值得注意的是,F1 分数达到了令人印象深刻的 98.88%。在单独评估时,ECG 的表现优于 PPG。我们的研究引入了一种新方法,将早期检测 HT 的舒适性和高准确性相结合,为该领域的发展做出了贡献。这种方法非常实用,可确保为患者提供友好的体验。
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引用次数: 0
Classification Model Using Transfer Learning for the Detection of Pneumonia in Chest X-Ray Images 利用迁移学习检测胸部 X 光图像中肺炎的分类模型
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.45277
G. Maquen-Niño, Jhojan Genaro Nuñez-Fernandez, Fany Yesica Taquila-Calderon, Ivan Adrianzén-Olano, Percy De-La-Cruz-VdV, Gilberto Carrión-Barco
In the current global context, there has been a significant increase in respiratory system diseases, particularly pneumonia. This disease has a higher incidence of mortality in children under five years old and adults over 60 years old because it leads to complications if not treated in time. This research leverages convolutional neural networks (CNNs) to classify images, specifically to detect the presence of pneumonia. The data processing methodology utilized in this study is CRISP-DM. The dataset consists of 5,856 images of anteroposterior chest X-rays downloaded from the open repository “Kaggle,” divided into 5,216 images for training, 16 for validation, and 624 for testing. Preprocessing involved image augmentation through modifications to the original images, scaling, and batch division in tensor format. A comparative analysis was conducted among the transfer models: DenseNet, VGG19, and ResNet50 version 2. Each transfer model was the header of a CNN with four subsequent layers. The models underwent training, validation, and testing phases. The test’s results showed that DenseNet achieved an accuracy of 0.87, VGG19 achieved 0.86, and ResNet50 achieved 0.91. These results affirm the effectiveness of ResNet50 in image classification, considering that the model’s output is binary, where 0 represents that the patient does not have pneumonia and 1 indicates that the patient has pneumonia.
在当前全球背景下,呼吸系统疾病,尤其是肺炎的发病率大幅上升。这种疾病对五岁以下儿童和 60 岁以上成人的致死率较高,因为如果不及时治疗会导致并发症。本研究利用卷积神经网络(CNN)对图像进行分类,特别是检测是否存在肺炎。本研究采用的数据处理方法是 CRISP-DM。数据集包括从开放存储库 "Kaggle "下载的 5856 张前胸部 X 光片图像,其中 5216 张用于训练,16 张用于验证,624 张用于测试。预处理包括通过修改原始图像、缩放和以张量格式批量分割来增强图像。对转移模型进行了比较分析:DenseNet、VGG19 和 ResNet50 版本 2。每个转移模型都是一个具有四个后续层的 CNN 的头部。这些模型经历了训练、验证和测试阶段。测试结果显示,DenseNet 的准确率为 0.87,VGG19 为 0.86,ResNet50 为 0.91。这些结果肯定了 ResNet50 在图像分类方面的有效性,因为该模型的输出是二进制的,0 表示患者没有肺炎,1 表示患者有肺炎。
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引用次数: 0
Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model 黄河流域水质强化预测:HHO-LSTM 模型的应用
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.48225
Minning Wu, Eric B. Blancaflor, Fei Ren, Yong Wang, Ting Dong
In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.
在中国黄河流域这一水资源枢纽地区,精确的水资源预测对于有效合理地管理水资源至关重要。本研究采用哈里斯-霍克斯优化-长短期记忆(HHO-LSTM)模型,提出了一种新的水资源预测方法。该方法克服了传统技术在处理时间序列数据和各种可变因素时所面临的限制。它包括对黄河流域多源水文数据收集过程的全面描述,以及细致的数据预处理。本研究的数据集包括四个关键水质参数的估算值,并通过均方误差 (MSE) 和均方根误差 (RMSE) 指标来衡量模型的有效性。这有助于利用历史水质数据预测特定区域的未来水质趋势。HHO-LSTM 模型在预测不同时间尺度和水资源变量的水质方面表现出卓越的准确性和鲁棒性,标志着黄河流域水资源管理的重大进步。这种方法不仅增强了当前的管理策略,还为正在进行的水资源研究和决策过程提供了宝贵的见解。
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引用次数: 0
Flexible Ureteroscopy Lithotripsy Operative Time Prediction Model for the Treatment of Kidney Stones 治疗肾结石的柔性输尿管镜碎石手术时间预测模型
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.43257
C. Baidada, Mustapha Aatila, M. Lachgar, Hamid Hrimech, Younes Ommane, Abderrahim Houlali
Effective time and resource management is crucial not only in the operating room but also in healthcare supply chains. Healthcare supply chains involve the movement of medical supplies, equipment, and medications from manufacturers to healthcare providers. Effective management is crucial to ensuring that patients receive the care they need promptly. In the operating room, it is essential to have an information process in place to effectively manage time and resources during the current surgical procedure. This paper focuses on developing a predictive model for the operating time of flexible ureteroscopy for kidney stones. The model can forecast surgical and preoperative time based on patient characteristics and surgeon experience. The model can assist in planning ureteroscopy procedures and preventing surgical complications, which is crucial not only for the operating room but also for healthcare supply chains. The paper presents a study that compares different feature selection methods and regression techniques. The study found that sequential backward selection combined with the extra tree regressor was the most effective approach.
有效的时间和资源管理不仅对手术室至关重要,对医疗供应链也同样重要。医疗保健供应链涉及医疗用品、设备和药品从制造商到医疗保健提供商的流动。有效的管理对于确保病人及时得到所需的治疗至关重要。在手术室中,必须建立一个信息流程,以有效管理当前手术过程中的时间和资源。本文主要针对肾结石柔性输尿管镜检查的手术时间建立一个预测模型。该模型可根据患者特征和外科医生经验预测手术和术前时间。该模型可协助规划输尿管镜检查手术和预防手术并发症,这不仅对手术室至关重要,对医疗保健供应链也至关重要。论文介绍了一项比较不同特征选择方法和回归技术的研究。研究发现,结合额外树回归器的顺序后向选择是最有效的方法。
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引用次数: 0
Remote Heart Rate Monitoring Device Using the Internet of Things 使用物联网的远程心率监测设备
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.46857
Orlando Iparraguirre-Villanueva, Enrique Surcco-Jacinto, Melanie Balvin-Chávez
Cardiovascular diseases are the leading cause of death worldwide. Therefore, this study aims to develop a mobile application utilizing the Internet of Things (IoT) to monitor patients’ heart rate. The study employed a quantitative approach and a pre-experimental design. The experiment was conducted according to the research plan and involved 20 patients. The Scrum methodology was used for the development of the mobile application. The results reveal a significant improvement in patient and family satisfaction after using the IoT-enabled mobile application. In addition, the average measurement time has decreased to 6.025 minutes, which represents a significant difference compared to the traditional method. The number of measurements has increased from seven to 14 per week, averaging two regular daily measurements. The measurement device has alleviated the concerns of family members who are taking care of loved ones with cardiovascular disease. This tool gives users greater peace of mind, enabling them to take accurate and reliable measurements 24/7.
心血管疾病是导致全球死亡的主要原因。因此,本研究旨在开发一款利用物联网(IoT)监测患者心率的移动应用程序。研究采用了定量方法和预实验设计。实验按照研究计划进行,共有 20 名患者参与。移动应用程序的开发采用了 Scrum 方法。结果显示,使用物联网移动应用程序后,患者和家属的满意度有了明显提高。此外,平均测量时间缩短至 6.025 分钟,与传统方法相比差异显著。测量次数从每周 7 次增加到 14 次,平均每天两次。测量设备减轻了照顾心血管疾病患者的家人的担忧。这一工具让用户更加放心,使他们能够全天候进行准确可靠的测量。
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引用次数: 0
An Intelligent Mathematics Problem-Solving Tutoring System Framework 智能数学问题解决辅导系统框架
Pub Date : 2024-03-15 DOI: 10.3991/ijoe.v20i05.47793
Mohamad Ariffin Abu Bakar, Ahmad Termimi Ab Ghani, Mohd Lazim Abdullah
This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks.
本研究提出了一个新颖的框架,用于重新设计智能辅导系统(ITS)中的问题解决活动,该系统被称为智能神经机制数学问题解决辅导系统(IN-MP-STS)。这篇概念论文提出了一种新的智能辅导系统方法,它将神经科学机制的元素作为一种学习策略,侧重于通过神经机制优化大脑的能力。它还引入了模糊神经网络(FNN)作为调节评估和分析结果的工具。这一框架为 ITS 模块中的授课方式和学习方法提供了另一种视角。通过有效整合动机、激活、调节、执行、记忆和互动等神经科学机制要素,可以实现深度学习,从而提高学生的能力。该框架还提出了基于 FNN 的自适应评估组件,这将增强系统中的测量和反馈模块。有必要修改智能学习系统和软计算方法(如神经网络研究)的结合方式,使学习测量和评估更加透明。这一创新尚未完全公开,因此鼓励研究人员进一步测试所提出的概念,以评估其与现有系统和伦理考虑的一致性。该框架增强了 FNN 的概念研究成果,并将基于神经科学的策略纳入了 ITS 模型中的架构和自主解决问题的技能。它还为问题解决学习的发展提供了参考。IN-MP-STS 有可能显著提高学生的能力和才干,从而促进更全面、整体和可持续的 ITS 的发展。这种方法还有可能丰富现有关于神经网络可持续性的文献。
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引用次数: 0
期刊
International Journal of Online and Biomedical Engineering (iJOE)
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